The previous DTI prediction methods can be mainly separated into two categories: similarity-based methods and feature-based methods. Similarity-based methods assume that similar drugs or proteins may have similar interaction patterns. These methods use many different similarity measures based on fingerprint, chemical structure, sequence data and so on to identify drug-target interaction. Feature-based methods solve drug–target interaction prediction as a binary classification problem, such as DeepConv-DTI [1]}, use known drug-target pairs as positive sample. To combine both the similarity and feature vectors, the neural network models require a mechanism to represent both the interaction patterns and the features of drug and protein sequences.
